1. Field of the Invention
The present invention relates to detection of defects in reticles used in the fabrication of integrated circuit chips. In particular, the invention concerns reticle defect detection using simulation of a photolithographic system.
2. Description of the Prior Art
Integrated circuits are made by photolithographic processes which use photomasks or reticles and an associated light source to project a circuit image onto a wafer. The light interacts with resist materials on the wafer to define various shapes and channels on the wafer. Then, after appropriate processing, the shapes and channels eventually define the electronic circuits on the wafer. Thus, reticles are used in the semiconductor manufacturing industry for the purpose of transferring photolithographic patterns onto a substrate, typically the wafer, during the manufacture of integrated circuits on the substrate. The wafer may be comprised of silicon, gallium arsenide or the like. In addition, photolithography is used to transfer patterns onto metal layers deposited on semiconductor substrates.
In the semiconductor industry, reticles are typically comprised of a polished transparent substrate, such as a fused quartz plate, on which a thin patterned opaque layer, consisting of figures, has been deposited on one surface. Usually, the patterned opaque layer is chromium. This layer may have a light anti-reflection coating deposited on one or both surfaces of the chromium.
In order to produce functioning integrated circuits at a high yield rate, the reticles need to be free of defects or, at least, free from defects that would adversely affect the photolithographic process or the resulting integrated circuit. A defect may be defined as any unintended modification to the intended photolithographic pattern caused during the manufacture of the reticle or as a result of the use of the reticle.
The following are a few of the possible defects: a portion of the opaque layer being absent from an area where it is intended to be present; a portion of the opaque layer being present in an area where it is not intended to be; chemical stains or residues from the reticle manufacturing processes causing an unintended localized modification of the light transmission property of the reticle; particulate contaminates such as dust, resist flakes, skin flakes; erosion of the photolithographic pattern due to electrostatic discharge; artifacts in the reticle substrate such as pits, scratches, and striations; and localized light transmission errors in the substrate or opaque layer. During the manufacture of reticles, inspection of the reticle is performed in order to detect the aforementioned and other defects.
Automated mask inspection systems have existed for many years. The earliest such system, the Bell Telephone Laboratories AMIS system (John Bruning et al., “An Automated Mask Inspection System—AMIS”, IEEE Transactions on Electron Devices, Vol. ED-22, No. 7 July 1971, pp 487 to 495), used a laser that scanned the mask. Subsequent systems used a linear sensor to inspect an image projected by the mask, such as described by Levy et al. in U.S. Pat. No. 4,247,203, “Automatic Photomask Inspection System and Apparatus” who teach die-to-die inspection, i.e., inspection of two adjacent dies by comparing them to each other. Alternately, in U.S. Pat. No. 4,926,489, (“Reticle Inspection System”) Danielson et al. teach die-to-database inspection, i.e. inspection of the reticle by comparison to the database containing data regarding the intended design from which the reticle was made. U.S. Pat. Nos. 4,247,203 and 4,926,489 are incorporated herein by reference as though set forth herein in full.
A first type of defect detection method is the die-to-die method. In this method, a first reticle is scanned by laser or other light source and the image of the first reticle is projected onto a sensor which reads the image. The image detection process is repeated for a second reticle where the second reticle is identical in design to the first reticle. Then, the resulting image of the first reticle is compared with the resulting image of the second reticle to detect defects. Typically, the second reticle is adjacent to the first reticle on the same wafer.
Another type of defect detection method is the die-to-database method. In this method, the first reticle is scanned, and the resulting image is compared to the information contained in the database defining the first reticle, in order to detect defects. Typically, the database is a CADS (Computer Aided Design System) containing the information used to create the reticle under examination. In such a case, the CADS database is converted to an image format before being compared to the scanned image of the first reticle. See U.S. Pat. No. 4,926,489, which is incorporated herein by reference as though set forth herein in full.
Both the die-to-die and die-to-database techniques typically involve performing predetermined methods for comparing the subject image data to the reference image data, and then identifying any substantial differences. This may be accomplished by actually calculating a difference image or else by comparing features of the two images. Generally, defect detection is performed automatically using a programmed computer workstation. The output of the software typically is a list of the identified defects, together with images of those defects.
The above-discussed defect detection methods thus provide reports of the defects of the reticle under examination, but do not provide much information to determine the printability of the defect. That is, the defect report itself does not provide sufficient information to determine whether or not each of the defects is likely to cause negative impact on the lithography process or to adversely affect the device performance. Currently, this determination usually is made exclusively by human operators examining each of the defects that were detected using the above-discussed processes. Determining defect printability in this manner generally is very time-consuming, inconsistent, and inaccurate because it relies purely on the human operator's experience and intuition. Typically, in each case the operator must use his judgment in determining, based on the defect size, type and location relative to patterns on the reticle, whether the defect is likely to cause a significant problem when the reticle is used in chip fabrication.
Errors in evaluating defect printability can be costly. For example, rather than risk a return of the reticle, mask shops often choose to clean or repair reticles when defects are found. If the defect would not in fact have printed as the operator predicted, such cleaning and repair introduces unnecessary delays and costs. In addition, unnecessary repair is risky because it may introduce serious defects which themselves can be irreparable, requiring the reticle to be scrapped. On the other hand, defects that are incorrectly judged to be inconsequential will show up later during fabrication, causing additional fabrication delays and expense.
Moreover, as the complexity of integrated circuits increases and feature sizes decrease, the number of defects has been increasing. This has occurred for a number of reasons. For example, higher resolution photolithography means that smaller defects are printable; as the image pixel size decreases, more defects become visible; also defect specification are becoming tighter. As a result, the number of defects per reticle has increased up to 800. It is noted that when optical proximity correction (OPC) is used, the sub-resolution sizes of the serifs and other elements of the reticle can compound the problem.
The foregoing developments have increased the demands on the inspection process. Both the need for resolving smaller defects and for inspecting larger areas are requiring much greater speed requirements, in terms of number of elements processed per second. Accordingly, it is becoming increasingly important to provide additional objective information for determining defect printability.
It therefore would be advantageous to automate or mechanize the defect printability determination by ascertaining the impact of each of the defects on the lithography process or device performance. Such a system or process would allow for early detection and repair of defects of reticles. Moreover, the system or the process would reduce the heavy reliance on human intuition and experience, and increase the confidence and reliability in the printability of reticles; thereby increasing the production yield of good circuits from the semiconductor wafers.